Large language model (LLM) outputs may produce inaccurate or irrelevant results when they rely solely on retrieval-based methods or generative AI models. This is why the retrieval-augmented generation (RAG) framework was developed. Retrieval-augmented generation can be applied to LLMs to enhance their performance in various natural language processing tasks. In this article, we’ll explain retrieval-augmented generation and explore the advantages of using it. We’ll also share specific use cases that illustrate its potential to deliver more accurate, context-aware responses in a variety of industry-specific use cases.
What Is Retrieval-Augmented Generation?
Generative models can create novel, context-aware outputs, but they often struggle to maintain accuracy, due in part to the difficulty of accessing information beyond the data the model was trained on. Retrieval-augmented generation combines retrieval-based methods with generative AI models to create something more powerful than its component parts. Retrieval-based systems are optimized for locating and extracting specific information from information that may not be part of the model’s training data. This can include online sources created after the training cut-off date, such as news articles, blogs, authoritative content websites and internal data sources such as proprietary documents and knowledge-base content.
To deploy such a RAG framework, teams need a combination of:
Client/app UI: End users are able to interact with the knowledge base, typically in the form of a chat service.
Context repository: Relevant data sources are aggregated, governed and continuously updated as needed to provide an up-to-date knowledge repository.
Vector search: The combination of a vector store maintains the numerical or vector representation of the knowledge base, with semantic search to provide easy retrieval of the chunks most relevant to the question.
LLM inference: This approach enables teams to embed the question and the context to find the most relevant information, and generate contextualized responses using a conversational LLM.
By combining both technologies, retrieval-augmented generation enables LLMs to reference authoritative sources before generating the requested output. Combining the strengths of both processes, RAG provides a solution that alleviates many of the issues that can hinder the effectiveness of LLMs.
Benefits of Using RAG to Improve the Accuracy and Reliability of LLMs
Large language models are remarkable for their ability to generate human-like responses and understand the content and intent behind natural language. Organizations use them in numerous applications, including content and code generation, language translation, summarization, and classification tasks such as sentiment analysis. Retrieval-augmented generation improves the accuracy and reliability of these tasks, allowing LLMs to perform their work to a higher standard.
Correcting many common problems with LLMs
On their own, LLMs can only access static training data, which rarely contains the most current, up-to-date information. And without specific guidance about which sources are authoritative and which ones aren’t, LLMs may select the non-authoritative source when generating a response or become confused when two sources used in model training use similar terms to describe different things. Lastly, when models don’t have a response to offer, they may choose to fabricate one or hallucinate. Retrieval-augmented generation alleviates these issues, providing better control over the sources the models reference, resulting in more accurate, authoritative responses.
Creating higher-quality outputs that can be tracked to a specific source
LLMs are only useful if they can be trusted to provide consistently reliable, authoritative responses. The RAG technique allows responses to be tracked back to specific references, and enables the inclusion of source citations as part of the response.
A cost-effective way to keep a knowledge repository updated
In many industries, data has a very short sell-by date. Retrieval-augmented generation allows developers to keep pre-trained models updated with information that wasn’t included in the original training data, providing an economical alternative to model fine-tuning. Using RAG, LLM models can be plugged into continuously updated sources such as live news, event or social media feeds, and financial, weather or IoT sensor data.
More control for model developers
RAG provides developers with more flexibility, allowing them to build more capable, fit-to-purpose solutions. Using retrieval-augmented generation, developers can limit access to sensitive information, allowing the model to use restricted information only when formulating a response for an individual authorized to view it.
Where Retrieval-Augmented Generation Techniques Are Used
RAG techniques have found a home in many applications that use natural language processing. As the use of LLMs increases, retrieval-augmented generation has become an essential component of many of these systems.
Internal knowledge base information
LLMs can be used to connect employees with proprietary information contained within a domain-specific knowledge base. These models allow them to ask specific questions using natural language and receive a response generated from the information found in the organization’s internal knowledge base. RAG can be used to enhance the accuracy and relevance of the answers provided and allows tailoring of the information included in the responses to the authorization level of the individual requesting it.
Healthcare information systems
Healthcare chatbots serve a range of functions, including disease diagnosis based on patient-provided symptoms, on-demand access to mental health resources, and help with appointment scheduling and prescription refills. In healthcare applications, providing accurate and reliable information is critical, with RAG playing an important part in connecting LLMs with authoritative sources of content.
Business intelligence
With access to continuously updated market information, retrieval-augmented generation can enhance the quality and currency of business-intelligence activities, allowing organizations to make informed decisions, identify opportunities and build a competitive advantage.
Intelligent customer support
LLM-powered customer service chatbots handle many routine tasks, including product support, issue resolution, claims processing and more. Retrieval-augmented generation provides live access to accurate, verified content, including product and order information and individual customer data — allowing chatbots to provide contextually relevant responses anchored to the most up-to-date information available.
Leverage Retrieval-Augmented Generation Natively Inside Snowflake with Snowflake Cortex
Snowflake Cortex, now in public preview, is an intelligent, fully managed service with access to industry-leading AI models, LLMs and vector search functionality. Using Cortex, organizations can build and deploy LLM apps that learn the unique nuances of their business and data in minutes, including LLM applications customized with their data using retrieval-augmented generation natively inside Snowflake. To learn more visit the Snowflake blog.